Is it ok to struggle with mathematics while learning AI as a beginner?
I think the key part of your question is "as a beginner". For all intents and purposes you can create a state of the art (SoTA) model in various fields with no knowledge of the mathematics what so ever.   

This means you do not need to understand back-propagation, gradient descent, or even mathematically how each layer works. Respectively you could just know there exists an optimizer and that different layers generally do different things (convolutions are good at picking up local dependencies, fully connected layers are good at picking up connections among your neurons in an expensive manner when you hold no previous assumptions), etc.. Follow some common intuitions and architectures built upon in the field and your ability to model will follow (thanks to the amazing work on opensource ML frameworks -- Looking at you Google and Facebook)! But this is only a stop-gap.  

A Newton quote that I'm about to butcher: "If I have seen further it's because I'm standing on the shoulders of giants". In other words he saw further because he didn't just use what people before him did, he utilized it to expand even further. So yes, I think you can finish your object detection project on time with little understanding of the math (look at the Google object detection API, it does wonders and you don't even need to know anything about ML to use it, you just need to have data. But, and this is a big but, if you ever want to extend into a realm that isn't particularly touched upon or push the envelope in any meaningful way, you will probably have to learn the math, learn the basics, learn the foundations.